Systems that interact directly with human beings are increasingly using artificial intelligence to recognize and generate human emotions. For example, personal digital assistants are increasingly using artificial intelligence to recognize human emotions so that they can respond more effectively to users. Similarly, customer relationship management (CRM) bots are increasingly using artificial intelligence to recognize human emotions to provide improved interactions with customers. But these systems often suffer from various technological problems. First, these systems often rely on seeing the entire user input to recognize human emotions. This need to see the entire user input can often be expensive in terms of memory usage and computation time. Second, these systems often suffer from a long delay between receiving the user input and actually recognizing human emotions contained therein. Finally, these systems are often poor at predicting the presence of various human emotions. For example, these systems are often unable to predict the presence of humor.